# How to speed up bambi model

**URL:** <https://discourse.pymc.io/t/how-to-speed-up-bambi-model/11827>\
**Category:** version agnostic\
**Tags:** bambi\
**Created:** [April 6, 2023, 8:49am UTC](https://discourse.pymc.io/t/how-to-speed-up-bambi-model/11827 "2023-04-06T08:49:05Z")\
**Posts on this page:** 3\
**Page:** 1

<div class="post-metadata">

**Author:** ![mihagazvoda](https://yyz2.discourse-cdn.com/flex036/user_avatar/discourse.pymc.io/mihagazvoda/32/5894_2.png) [@mihagazvoda](https://discourse.pymc.io/u/mihagazvoda)\
**Post date:** [April 6, 2023, 8:49am UTC](https://discourse.pymc.io/t/how-to-speed-up-bambi-model/11827/1 "2023-04-06T08:49:05Z")

</div>

Hey! I have a model of aggregated binomial data per group. Together, I have ~50k groups and in each group thousands of trials (with low number of successes). So my data looks something like:

```auto
import pandas as pd
import bambi as bmb # version 0.10.0

df_simple = pd.DataFrame({
    'group': ['A', 'B', 'C'],
    'y': [10, 20, 30],
    'n': [100000, 5000, 900000]
})

m = bmb.Model('p(y, n) ~ (1|group)', data=df_simple, family='binomial')
idata = m.fit()

```

I also use informative priors. What are the options for improving the performance of the model? I’m using `inference_method="nuts_blackjax"` on CPU which seems a bit faster on the subset than “standard” MCMC. Any other recommendations or resources? Thank you!

---

<div class="post-metadata">

**Author:** ![tcapretto](https://yyz2.discourse-cdn.com/flex036/user_avatar/discourse.pymc.io/tcapretto/32/4902_2.png) [@tcapretto](https://discourse.pymc.io/u/tcapretto)\
**Post date:** [April 6, 2023, 2:00pm UTC](https://discourse.pymc.io/t/how-to-speed-up-bambi-model/11827/2 "2023-04-06T14:00:22Z")

</div>

Hi Miha!

If your model only has the `group` predictor, the PyMC wouldn’t be too complicated. I think you can do something like

```python
y = df_simple["y"].to_numpy()
n = df_simple["n"].to_numpy()
groups, groups_idx = np.unique(df_simple["group"])
coords = {"group": group}
with pm.Model(coords=coords) as model:
    intercept_mu = pm.Normal("intercept_mu", mu=0, sigma=1)
    intercept_sigma = pm.HalfNormal("intercept_sigma", sigma=1)
    intercept_offset = pm.Normal("intercept_offset", dims="group")
    intercept = pm.Deterministic("intercept", intercept_mu + intercept_sigma * intercept_offset)
    p = pm.math.invlogit(intercept)
    pm.Binomial("outcome", p=p, n=n, observed=y)

```

which uses a non-centered parameterization.

This PyMC model is equivalent to the Bambi model and perhaps is faster.

---

<div class="post-metadata">

**Author:** ![mihagazvoda](https://yyz2.discourse-cdn.com/flex036/user_avatar/discourse.pymc.io/mihagazvoda/32/5894_2.png) [@mihagazvoda](https://discourse.pymc.io/u/mihagazvoda)\
**Post date:** [April 9, 2023, 9:38pm UTC](https://discourse.pymc.io/t/how-to-speed-up-bambi-model/11827/3 "2023-04-09T21:38:41Z")

</div>

Thank you, will try!